Setup
Load libraries
library(ggplot2)
library(tidyr)
library(dplyr)
library(Matrix)
library(Seurat)
library(cowplot)
library(patchwork)
# parallelization
library(future)
options(future.globals.maxSize= +Inf)
plan()
sequential:
- args: function (expr, envir = parent.frame(), substitute = TRUE, lazy = FALSE, seed = NULL, globals = TRUE, local = TRUE, earlySignal = FALSE, label = NULL, ...)
- tweaked: FALSE
- call: NULL
Process Human Data
import_remote_data <- function(file_url, type = "table", header = FALSE) {
con <- gzcon(url(file_url))
txt <- readLines(con)
if (type == "MM") { return (readMM(textConnection(txt))) }
if (type == "table") { return (read.table(textConnection(txt), header = header)) }
}
count_matrix_URL <- "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137537/suppl/GSE137537_counts.mtx.gz"
gene_names_URL <- "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137537/suppl/GSE137537_gene_names.txt.gz"
sample_annotations_URL <- "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137537/suppl/GSE137537_sample_annotations.tsv.gz"
human.count_matrix <- as.matrix(import_remote_data(count_matrix_URL, type = "MM"))
human.gene_names <- import_remote_data(gene_names_URL, type = "table")
human.sample_annotations <- import_remote_data(sample_annotations_URL, type = "table", header = TRUE)
human_ret_seurat
An object of class Seurat
19712 features across 20091 samples within 1 assay
Active assay: RNA (19712 features, 0 variable features)
Process Mouse Data
mouse.data <- Read10X(data.dir = "filtered_feature_bc_matrix")
dimnames(mouse.data)[[1]] <- tolower(dimnames(mouse.data)[[1]])
dimnames(mouse.data)[[2]] <- tolower(dimnames(mouse.data)[[2]])
mouse_ret_seurat <- CreateSeuratObject(counts = mouse.data,
project = "mouse_ret",
min.cells = 3,
min.features = 200)
mouse_ret_seurat
An object of class Seurat
16424 features across 4510 samples within 1 assay
Active assay: RNA (16424 features, 0 variable features)
Process Primate Data
url=https://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118546/suppl/GSE118546_macaque_fovea_all_10X_Jan2018.Rdata.gz
wget $url -O primate_data/GSE118546_macaque_fovea_all_10X_Jan2018.Rdata.gz
gunzip primate_data/*
install.packages( c('devtools', 'roxygen2') )
library(devtools)
library(roxygen2)
install_github( 'hb-gitified/cellrangerRkit',
auth_token = 'your_token' )
macaque_fovea_seurat
An object of class Seurat
30039 features across 111993 samples within 1 assay
Active assay: RNA (30039 features, 0 variable features)
Cleanup
rm(human.count_matrix, human.gene_names, human.sample_annotations)
rm(count_matrix_URL, gene_names_URL, sample_annotations_URL, import_remote_data)
rm(mouse.data)
rm(Count.mat_fovea, macaque_fovea)
Combine
# combine
ret.list <- list(human = human_ret_seurat, mouse = mouse_ret_seurat, macaque = macaque_fovea_seurat)
# preprocess
ret.list <- lapply(X = ret.list, FUN = function(x) {
x <- NormalizeData(x, verbose = FALSE)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000, verbose = FALSE)
})
# cleanup
rm(human_ret_seurat, mouse_ret_seurat, macaque_fovea_seurat)
Integration
plan("multiprocess", workers = 4)
ret.anchors <- FindIntegrationAnchors(object.list = ret.list, dims = 1:50, anchor.features = 1000)
plan("multiprocess", workers = 1)
ret.combined <- IntegrateData(anchorset = ret.anchors, dims = 1:50)
Integrated Analysis
plan("multiprocess", workers = 4)
DefaultAssay(ret.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
ret.combined <- ScaleData(ret.combined, verbose = FALSE)
ret.combined <- RunPCA(ret.combined, npcs = 50, verbose = FALSE)
# t-SNE and Clustering
ret.combined <- RunUMAP(ret.combined, reduction = "pca", dims = 1:35)
ret.combined <- FindNeighbors(ret.combined, reduction = "pca", dims = 1:35)
ret.combined <- FindClusters(ret.combined, resolution = 0.075)
UMAP Visualization
DimPlot(ret.combined, reduction = "umap", group.by = "orig.ident")

DimPlot(ret.combined, reduction = "umap", label = TRUE)

DimPlot(ret.combined, reduction = "umap", split.by = "orig.ident", ncol = 1)

Identify Clusters with Canonical Markers
DefaultAssay(ret.combined) <- "RNA"
features <- tolower(c("Pde6a","Gnat2","Nefl","Camk2b","Thy1","Gad1","Slc6a9",
"Pcsk6","Trpm1","Sept4","Glul","Arr3","C1qa","Tm4sf1", "Mgp"))
FeaturePlot(object = ret.combined,
features = features,
pt.size = 0.1,
cols = c("lightgrey", "#F26969"),
min.cutoff = "q9",
combine = TRUE) & NoLegend() & NoAxes()

# for(i in 1:length(p)) {
# p[[i]] <- p[[i]] + NoLegend() + NoAxes()
# }
#
# cowplot::plot_grid(plotlist = p, ncol=3)
- Rod : pde6a
- AC (amacrine cell) : gad1, slc6a9
- MG (Müller glia) : glul
- BC (bipolar cell) : Trpm, camk2b
- CC (cone cell) : gnat2, arr3
- RGC (retinal ganglial cell) : nefl, thy1
- VC (vascular cell) : mgp, tm4sf1
- M (microglia) : c1qa
- HC (horizontal cell) : sept4
Markers were determined from this paper and other sources.
ret.combined <- RenameIdents(ret.combined, `0` = "MG", `1` = "Rod", `2` = "RGC",
`3` = "RGC", `4` = "BC", `5` = "CC", `6` = "BC", `7` = "AC", `8` = "BC", `9` = "RGC",
`10` = "RGC", `11`= "HC", `12` = "MG", `13` = "VC", `14` = "RGC", `15` = "RGC", `16` = "M", `17` = "RGC")
DimPlot(ret.combined, label = TRUE)

Find Differentially Expressed Genes

ret.combined$celltype.organism <- paste(Idents(ret.combined), ret.combined$orig.ident, sep = "_")
ret.combined$celltype <- Idents(ret.combined)
Idents(ret.combined) <- "celltype.organism"
cells.diffgenes <- as.list(cells.types)
cells.diffgenes <- lapply(cells.diffgenes, FUN = function(x) {
lab_human <- sprintf("%s_human_ret", x)
lab_mouse <- sprintf("%s_mouse_ret", x)
return(FindMarkers(ret.combined, ident.1 = lab_human, ident.2 = lab_mouse, verbose = FALSE))
})
Tables with the most differentially expressed genes in each cell subtype:
for(i in seq_along(cells.diffgenes)) {
print(knitr::kable(head(cells.diffgenes[[i]]),caption=cells.types[[i]]))
}
Rod
| ckb |
0 |
1.4493770 |
0.918 |
0.724 |
0 |
| hsp90aa1 |
0 |
1.3457646 |
0.854 |
0.627 |
0 |
| nrl |
0 |
1.3140138 |
0.874 |
0.635 |
0 |
| 0610009b22rik |
0 |
-0.6622860 |
0.000 |
0.130 |
0 |
| gm17018 |
0 |
-0.6831275 |
0.000 |
0.130 |
0 |
| spata1 |
0 |
-0.6929677 |
0.000 |
0.132 |
0 |
BC
| neat1 |
0 |
3.086391 |
0.793 |
0.064 |
0 |
| mtch1 |
0 |
-1.305054 |
0.000 |
0.459 |
0 |
| selenom |
0 |
-1.338108 |
0.000 |
0.480 |
0 |
| araf |
0 |
-1.342891 |
0.013 |
0.494 |
0 |
| klc3 |
0 |
-1.424615 |
0.002 |
0.500 |
0 |
| pea15a |
0 |
-1.427543 |
0.000 |
0.500 |
0 |
MG
| tf |
0 |
5.089073 |
0.962 |
0.000 |
0 |
| spp1 |
0 |
3.879036 |
0.847 |
0.003 |
0 |
| crabp1 |
0 |
3.865908 |
0.876 |
0.028 |
0 |
| gpx3 |
0 |
3.736219 |
0.869 |
0.052 |
0 |
| ftl |
0 |
3.672007 |
0.877 |
0.000 |
0 |
| actg1 |
0 |
3.639157 |
0.905 |
0.026 |
0 |
RGC
| mt-nd4 |
0 |
-5.434720 |
0 |
1 |
2e-06 |
| mt-nd5 |
0 |
-4.555808 |
0 |
1 |
2e-06 |
| mt-co1 |
0 |
-4.634061 |
0 |
1 |
2e-06 |
| malat1 |
0 |
-5.358199 |
0 |
1 |
2e-06 |
| mt-nd1 |
0 |
-5.600755 |
0 |
1 |
2e-06 |
| mt-nd2 |
0 |
-5.700498 |
0 |
1 |
2e-06 |
CC
| gm42418 |
0 |
-5.444663 |
0 |
1 |
0 |
| malat1 |
0 |
-5.893437 |
0 |
1 |
0 |
| mt-cytb |
0 |
-6.148057 |
0 |
1 |
0 |
| mt-co1 |
0 |
-4.052888 |
0 |
1 |
0 |
| mt-nd5 |
0 |
-4.170730 |
0 |
1 |
0 |
| mt-nd1 |
0 |
-4.734329 |
0 |
1 |
0 |
AC
| mt-nd5 |
0 |
-3.999182 |
0 |
1 |
0 |
| gm42418 |
0 |
-5.868449 |
0 |
1 |
0 |
| mt-co1 |
0 |
-4.023145 |
0 |
1 |
0 |
| mt-nd4 |
0 |
-5.035458 |
0 |
1 |
0 |
| mt-nd1 |
0 |
-5.173153 |
0 |
1 |
0 |
| mt-nd2 |
0 |
-5.370662 |
0 |
1 |
0 |
VC
| hla-b |
0 |
3.429125 |
0.884 |
0.00 |
0 |
| rps3a |
0 |
2.938618 |
0.826 |
0.00 |
0 |
| hla-e |
0 |
3.220179 |
0.826 |
0.00 |
0 |
| hla-a |
0 |
3.030967 |
0.812 |
0.00 |
0 |
| hla-c |
0 |
2.913989 |
0.797 |
0.00 |
0 |
| a2m |
0 |
3.322554 |
0.797 |
0.01 |
0 |
HC
| mt-nd5 |
0 |
-4.723687 |
0 |
1 |
0 |
| mt-co1 |
0 |
-4.915798 |
0 |
1 |
0 |
| mt-nd4 |
0 |
-5.757623 |
0 |
1 |
0 |
| mt-nd1 |
0 |
-5.944134 |
0 |
1 |
0 |
| gm42418 |
0 |
-6.046691 |
0 |
1 |
0 |
| mt-nd2 |
0 |
-6.094723 |
0 |
1 |
0 |
M
| ftl |
0 |
4.612295 |
0.98 |
0 |
0 |
| hla-dra |
0 |
4.664191 |
0.94 |
0 |
0 |
| hla-a |
0 |
2.899218 |
0.94 |
0 |
0 |
| hla-drb1 |
0 |
4.096260 |
0.92 |
0 |
0 |
| rps3a |
0 |
3.397725 |
0.92 |
0 |
0 |
| hla-b |
0 |
3.163581 |
0.92 |
0 |
0 |
Save as csv files
for(i in seq_along(cells.diffgenes)) {
write.csv(cells.diffgenes[[i]], sprintf("results/%d_%s.csv", i, cells.types[[i]]))
}
genes_to_plot <- 3
for (i in seq_along(cells.types)) {
print(FeaturePlot(object = ret.combined,
features = rownames(cells.diffgenes[[i]])[1:genes_to_plot],
split.by = "orig.ident",
max.cutoff = 3,
cols = c("grey", "red"),
pt.size = 0.07,
combine = TRUE,
label.size = 0.5
) + plot_annotation(title = cells.types[[i]]) & NoLegend() & NoAxes()
)
}









Check cell proportion for each species:
knitr::kable(prop.table(x = table(Idents(ret.combined), ret.combined@meta.data$orig.ident), margin = 2))
| 0 |
0.2627047 |
0.1535810 |
0.2875831 |
| 1 |
0.5498980 |
0.0758172 |
0.3164080 |
| 2 |
0.0001493 |
0.1855205 |
0.0002217 |
| 3 |
0.0009955 |
0.1458216 |
0.0035477 |
| 4 |
0.0531581 |
0.0808176 |
0.0838137 |
| 5 |
0.0114977 |
0.0783888 |
0.0388027 |
| 6 |
0.0406650 |
0.0552267 |
0.0569845 |
| 7 |
0.0187148 |
0.0577625 |
0.0343681 |
| 8 |
0.0484794 |
0.0443242 |
0.0917960 |
| 9 |
0.0001493 |
0.0342075 |
0.0000000 |
| 10 |
0.0000498 |
0.0232247 |
0.0004435 |
| 11 |
0.0076154 |
0.0156081 |
0.0152993 |
| 12 |
0.0000000 |
0.0117775 |
0.0000000 |
| 13 |
0.0034344 |
0.0089827 |
0.0436807 |
| 14 |
0.0000000 |
0.0109203 |
0.0000000 |
| 15 |
0.0000000 |
0.0101435 |
0.0008869 |
| 16 |
0.0024887 |
0.0039645 |
0.0261641 |
| 17 |
0.0000000 |
0.0039110 |
0.0000000 |
---
title: "Integrating Primate Data into Analysis"
output: html_notebook
---
# Setup
Load libraries
```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(tidyr)
library(dplyr)
library(Matrix)
library(Seurat)
library(cowplot)
library(patchwork)

# parallelization
library(future)
options(future.globals.maxSize= +Inf)
plan()
```
Process Human Data
```{r}
import_remote_data <- function(file_url, type = "table", header = FALSE) {
  con <- gzcon(url(file_url))
  txt <- readLines(con)
  if (type == "MM") { return (readMM(textConnection(txt))) }
  if (type == "table") { return (read.table(textConnection(txt), header = header)) }
}
count_matrix_URL <- "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137537/suppl/GSE137537_counts.mtx.gz"
gene_names_URL <- "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137537/suppl/GSE137537_gene_names.txt.gz"
sample_annotations_URL <- "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137537/suppl/GSE137537_sample_annotations.tsv.gz"

human.count_matrix <- as.matrix(import_remote_data(count_matrix_URL, type = "MM"))
human.gene_names <- import_remote_data(gene_names_URL, type = "table")
human.sample_annotations <- import_remote_data(sample_annotations_URL, type = "table", header = TRUE)
```
```{r}
rownames(human.count_matrix) <- tolower(human.gene_names[,1])
colnames(human.count_matrix) <- tolower(human.sample_annotations[,1])

human_ret_seurat <- CreateSeuratObject(counts = human.count_matrix, 
                                       meta.data = human.sample_annotations, 
                                       project = "human_ret", 
                                       min.cells = 3, 
                                       min.features = 200)
human_ret_seurat
```

Process Mouse Data
```{r}
mouse.data <- Read10X(data.dir = "filtered_feature_bc_matrix")
dimnames(mouse.data)[[1]] <- tolower(dimnames(mouse.data)[[1]])
dimnames(mouse.data)[[2]] <- tolower(dimnames(mouse.data)[[2]])
mouse_ret_seurat <- CreateSeuratObject(counts = mouse.data, 
                                       project = "mouse_ret", 
                                       min.cells = 3, 
                                       min.features = 200)
mouse_ret_seurat
```

Process Primate Data
```{bash}
url=https://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118546/suppl/GSE118546_macaque_fovea_all_10X_Jan2018.Rdata.gz
wget $url -O primate_data/GSE118546_macaque_fovea_all_10X_Jan2018.Rdata.gz
gunzip primate_data/*
```
```{r}
install.packages( c('devtools', 'roxygen2') )
library(devtools)
library(roxygen2)
install_github( 'hb-gitified/cellrangerRkit',
                auth_token = 'your_token' )
```
```{r}
load("primate_data/GSE118546_macaque_fovea_all_10X_Jan2018.Rdata")

dimnames(Count.mat_fovea)[[1]] <- tolower(dimnames(Count.mat_fovea)[[1]])
macaque_fovea_seurat <- CreateSeuratObject(Count.mat_fovea,
                                           project = "macaque_fovea", 
                                           min.cells = 3, 
                                           min.features = 200)

# give macaque dta uniform name in "orig.ident" metadata column
AddMetaData(macaque_fovea_seurat, 
            metadata = macaque_fovea_seurat[["orig.ident"]], 
            col.name = "orig.sample.name")
macaque_fovea_seurat[["orig.ident"]] <- "macaque_fovea"

macaque_fovea_seurat
```
Cleanup
```{r}
rm(human.count_matrix, human.gene_names, human.sample_annotations)
rm(count_matrix_URL, gene_names_URL, sample_annotations_URL, import_remote_data)
rm(mouse.data)
rm(Count.mat_fovea, macaque_fovea)
```


Combine
```{r}
# combine
ret.list <- list(human = human_ret_seurat, mouse = mouse_ret_seurat, macaque = macaque_fovea_seurat)

# preprocess
ret.list <- lapply(X = ret.list, FUN = function(x) {
    x <- NormalizeData(x, verbose = FALSE)
    x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000, verbose = FALSE)
})

# cleanup
rm(human_ret_seurat, mouse_ret_seurat, macaque_fovea_seurat)
```

# Integration
```{r}
plan("multiprocess", workers = 4)
ret.anchors <- FindIntegrationAnchors(object.list = ret.list, dims = 1:50,  anchor.features = 1000)
plan("multiprocess", workers = 1)
ret.combined <- IntegrateData(anchorset = ret.anchors, dims = 1:50)
```

# Integrated Analysis
```{r}
plan("multiprocess", workers = 4)

DefaultAssay(ret.combined) <- "integrated"

# Run the standard workflow for visualization and clustering
ret.combined <- ScaleData(ret.combined, verbose = FALSE)
ret.combined <- RunPCA(ret.combined, npcs = 50, verbose = FALSE)
# t-SNE and Clustering
ret.combined <- RunUMAP(ret.combined, reduction = "pca", dims = 1:35)
ret.combined <- FindNeighbors(ret.combined, reduction = "pca", dims = 1:35)
ret.combined <- FindClusters(ret.combined, resolution = 0.075)
```
# UMAP Visualization
```{r warning=FALSE}
DimPlot(ret.combined, reduction = "umap", group.by = "orig.ident")
DimPlot(ret.combined, reduction = "umap", label = TRUE)
```
```{r, fig.height = 4, fig.width = 3}
DimPlot(ret.combined, reduction = "umap", split.by = "orig.ident", ncol = 1)
```

# Identify Clusters with Canonical Markers
```{r}
DefaultAssay(ret.combined) <- "RNA"

features <- tolower(c("Pde6a","Gnat2","Nefl","Camk2b","Thy1","Gad1","Slc6a9",
                      "Pcsk6","Trpm1","Sept4","Glul","Arr3","C1qa","Tm4sf1", "Mgp"))

FeaturePlot(object = ret.combined, 
            features = features, 
            pt.size = 0.1,
            cols = c("lightgrey", "#F26969"),
            min.cutoff = "q9",
            combine = TRUE) & NoLegend() & NoAxes

# Cowplot method: make sure to change to "combine = FALSE" and remove "& NoLegend() & NoAxes"

# for(i in 1:length(p)) {
#   p[[i]] <- p[[i]] + NoLegend() + NoAxes()
# }
# 
# cowplot::plot_grid(plotlist = p, ncol=3)
```

* Rod : pde6a
* AC (amacrine cell) : gad1, slc6a9
* MG (Müller glia) : glul
* BC (bipolar cell) : Trpm, camk2b
* CC (cone cell) : gnat2, arr3
* RGC (retinal ganglial cell) : nefl, thy1
* VC (vascular cell) : mgp, tm4sf1
* M (microglia) : c1qa
* HC (horizontal cell) : sept4

Markers were determined from [this](https://www.nature.com/articles/s41467-019-12780-8) paper and other sources.
```{r}
ret.combined <- RenameIdents(ret.combined, `0` = "MG", `1` = "Rod", `2` = "RGC", 
    `3` = "RGC", `4` = "BC", `5` = "CC", `6` = "BC", `7` = "AC", `8` = "BC", `9` = "RGC", 
    `10` = "RGC", `11`= "HC", `12` = "MG", `13` = "VC", `14` = "RGC", `15` = "RGC", `16` = "M", `17` = "RGC")

DimPlot(ret.combined, label = TRUE)
```


# Find Differentially Expressed Genes
```{r}
cells.types <- c("Rod", "BC", "MG", "RGC", "CC", "AC", "VC", "HC", "M")
theme_set(theme_cowplot())

cell_type_avg <- function(seurat.combined, ident) {
  cells.x <- subset(seurat.combined, idents = ident)
  Idents(cells.x) <- "orig.ident"
  cells.x.avg <- log1p(AverageExpression(cells.x, verbose = FALSE)$RNA)
  cells.x.avg$gene <- rownames(cells.x.avg)
  return(cells.x.avg)
}

cells.plot <- as.list(cells.types)
cells.plot <- lapply(cells.plot, FUN = function(x) {
  cells.x.avg <- cell_type_avg(ret.combined, ident = x)
  x <- ggplot(cells.x.avg, aes(human_ret, mouse_ret)) + geom_point(size = 0.1) + ggtitle(x)
  return(x)
})

# For individual plots
# for (p in cells.plot) {
#   print(p)
# }

# For grid plot
cowplot::plot_grid(plotlist = cells.plot, ncol = 3)
```
```{r}
ret.combined$celltype.organism <- paste(Idents(ret.combined), ret.combined$orig.ident, sep = "_")
ret.combined$celltype <- Idents(ret.combined)
Idents(ret.combined) <- "celltype.organism"
```
```{r}
cells.diffgenes <- as.list(cells.types)
cells.diffgenes <- lapply(cells.diffgenes, FUN = function(x) {
  lab_human <- sprintf("%s_human_ret", x)
  lab_mouse <- sprintf("%s_mouse_ret", x)
  return(FindMarkers(ret.combined, ident.1 = lab_human, ident.2 = lab_mouse, verbose = FALSE))
})
```
Tables with the most differentially expressed genes in each cell subtype:
```{r}
for(i in seq_along(cells.diffgenes)) {
  print(knitr::kable(head(cells.diffgenes[[i]]),caption=cells.types[[i]]))
}
```
Save as csv files
```{r}
for(i in seq_along(cells.diffgenes)) {
  write.csv(cells.diffgenes[[i]], sprintf("results/%d_%s.csv", i, cells.types[[i]]))
}
```

```{r warning=FALSE}
genes_to_plot <- 3
for (i in seq_along(cells.types)) {
  print(FeaturePlot(object = ret.combined, 
              features = rownames(cells.diffgenes[[i]])[1:genes_to_plot], 
              split.by = "orig.ident", 
              max.cutoff = 3, 
              cols = c("grey", "red"),
              pt.size = 0.07,
              combine = TRUE,
              label.size = 0.5
              ) + plot_annotation(title = cells.types[[i]]) & NoLegend() & NoAxes()
        )
}
```

Check cell proportion for each species:
```{r}
knitr::kable(prop.table(x = table(Idents(ret.combined), ret.combined@meta.data$orig.ident), margin = 2))
```

